title: Alternative common factor models for multivariate biometric analyses.
abstract: In prior research we have shown how linear structural equation models and computer programs (e.g., LISREL) may be simply and directly used to provide alternatives for the traditional biometric twin design. We use structural equations and path models to define biometric group differences, we write traditional common-factor models in the same way, and then we take a detailed look at some alternative multivariate and biometric models. We contrast the biometric-factors covariance structure approach used by Loehlin and Vandenberg (1968), Martin and Eaves (1977), and others with the psychometric-factors approach used by McArdle et al. (1980) and others. We use the multivariate primary mental abilities data on monozygotic (MZ) and dizygotic (DZ) twins from Loehlin and Vandenberg (1968) to detail fundamental differences in model specification and results. We extend both multivariate biometric approaches using exploratory and confirmatory multiple-factor models. These comparisons show that each alternative multivariate methodology has useful features for empirical applications.
authors.affiliation: Department of Psychology, University of Virginia, Charlottesville 22903.
authors.forename: J J
authors.initials: JJ
authors.lastname: McArdle
authors.forename: H H
authors.initials: HH
authors.lastname: Goldsmith
mesh_terms: D006801:Humans; D007360:Intelligence; D008957:Models, Genetic; D015233:Models, Statistical; D015999:Multivariate Analysis; D012984:Software; D014427:Twins / Q000235:genetics* / Q000523:psychology